device = None
# dataset
DATASET_NAME = 'simpleai/HC3-Chinese'
DATASET_SUBSET = 'all'

# pretrained model

# We have downloaded offline version of the model.
# Related information will be provided in the paper
PRETRAINED_BERT_PATH = "../pretrain_model/bert-base-chinese"
PRESET_STOPWORDS = "../pretrain_model/cn-stopwords/cn_stopwords.txt"

# neuron network
lstm_hidden_size = 128
lstm_n_layers = 2
stat_n_features = 12
fusion_n_out = 32
classifier_dropout = 0.3
out_n_classes = 2
batch_size = 32

# training
learning_rate = 2e-5
save_after_train = 1  # 1=save state;2=save all (deprecated, use 1)
save_frequency = 5
random_seed = 42  # if somewhere needs a seed, it will use this
max_epoch = 30
loss_record_frequency = 10

# evaluation
test_size = 0.2
eval_model_path = "./model_state_checkpoint5.pth"


def getEvalDataset():
    from data import loadTestDs
    return loadTestDs()


# KF Cross Validation
K = 5
v_random_seed = 43
fold_max_epoch = 10
v_batch_size = 32
v_lr=1e-3

# Comparioson
c_random_seed=42
n_ft_afterSVD=300
tfidf_maxFt=10000